Diagnostic Fit Call

Book a Diagnostic Fit Call

Book a call to choose the right diagnostic, AI-Ready Data Diagnostic, or scoped build path for a messy marketing, product, or revenue data problem.

You do not need a polished brief. Bring the version of the problem that is slowing decisions down right now.

60% → 95%

Attribution coverage improved for a mid-market SaaS team after the reporting logic was rebuilt around revenue reality.

99%+

Pipeline uptime achieved after replacing brittle transformations with a tested dbt foundation.

18%

Churn reduction achieved in three weeks when warehouse data was operationalized into a real workflow.

What to expect on the call

30 minutes, focused

We spend the time on the decision that is stuck: which number is not trusted, which workflow is blocked, or which AI / measurement use case is not ready for real operating pressure.

A fast read on the right diagnostic rung

Some teams need a light entry diagnostic around one question. Others need the full AI-Ready Data Diagnostic because source trust, metric certification, entity resolution, and workflow readiness are tangled together. We will separate those paths instead of forcing one package.

A practical next step

If there is a fit, you leave with the clearest next move — entry diagnostic, flagship diagnostic, scoped build, optional run path, or a recommendation not to overcomplicate the problem yet.

This call is most useful when...

  • marketing, finance, product, and RevOps are defending different versions of the same number
  • you need to explain channel performance, pipeline quality, or product-led motion to leadership without caveats
  • your team has enough data to be dangerous but not enough trust to move quickly
  • AI pressure is rising and you are not convinced the source data, metric definitions, or workflow ownership are ready

If the problem is smaller than a consulting engagement, that is still a useful outcome. A clear "not yet" is better than forcing a project.

Choose a time

Pick a slot that works. If you would rather send context first, email [email protected].

Before you book

Use the booking notes, or send a short email to [email protected], with the line that best matches why you are reaching out:

  • Entry diagnostic: one narrow question needs a quick, practical read — for example attribution visibility, a reporting trust break, or whether AI-referred traffic is showing up anywhere useful.
  • AI-Ready Data Diagnostic: the problem spans source trust, governed metric definitions, entity resolution, workflow ownership, and whether AI or automation should touch live marketing/product/revenue work.
  • Build path: you already know the diagnostic answer and need help certifying metrics, proving lift, or operationalizing trusted data.

Managed Run + Measure context

If you are asking about Managed Run + Measure, something should already be live and worth protecting. Include what is running, who owns it, what needs monitoring, the leadership or workflow cadence it supports, and what would break if it drifted.

If you want the free AI-traffic read, say that directly. A screenshot or export of referral/source traffic is enough for the first pass. We will not overstate the percentage; the goal is to tell whether your current measurement can see the signal at all.

The kinds of outcomes these conversations usually unlock

Not vanity quotes. These are the kinds of business outcomes that happen when the underlying data problem gets named correctly and fixed in the right order.

Names are withheld here because these conversations often start before a client wants public attribution, but each example below maps to a published case study so you can see the kind of work behind the outcome.

60% → 95% attribution coverage

One number marketing and finance could both defend

We went from defending numbers in every board meeting to making budget allocation decisions in hours.

VP of Growth

300-person B2B SaaS company with a 90-day sales cycle

Read the attribution case study

99%+ pipeline uptime

A data foundation the team stopped babysitting

Our team went from constant firefighting to barely thinking about pipeline reliability.

Head of Data

200-person mid-market SaaS team with a brittle dbt stack

Read the pipeline reliability case study

18% churn reduction in 3 weeks

A fast win tied to a real workflow

Domain Methods shipped a reverse ETL workflow in three weeks that moved the needle immediately.

Head of Product

PLG SaaS business with 15,000 active accounts

Read the data activation case study

Questions people usually have before they book

Is this a sales call?

No. The point is to understand the operating problem, pressure-test the likely root cause, and decide whether there is a sensible next step. If there is not, I will say that directly.

Which diagnostic should we ask about?

If the question is narrow — for example one reporting dispute, one attribution read, or one AI-traffic check — start with an entry diagnostic conversation. If the problem spans source systems, certified metrics, executive reporting, and AI/workflow readiness, the AI-Ready Data Diagnostic is the better fit because it maps the foundation below the AI and the measurement above it.

How does the free AI-traffic read work?

Mention the AI-traffic read when you book or send context by email. Bring a GA4 or analytics referral-source view if you have one. We will give you a directional read on what appears to be AI-referred, what is probably hidden by current attribution, and whether the issue is worth a deeper diagnostic.

Who should join the call?

Usually the person who owns the pain and one person who sees the data side clearly. That might be a VP of Growth and RevOps lead, a Head of Data and operator, a product/growth lead, or a founder plus the person carrying reporting debt every week.

What should we bring?

Bring the version of the problem you are actually arguing about now: the dashboard nobody trusts, the channel question you cannot answer, the board metric that keeps changing, the AI use case that feels premature, or the product/revenue workflow that should be automated but is not trusted yet. If you are asking about Managed Run + Measure, also bring what is already live, who owns it, what needs monitoring, and what would break if it drifted.

Do we need to grant system access before the call?

No. The first conversation does not require direct access to your CRM, warehouse, ad platforms, billing system, product analytics, or production tools. Screenshots, exports, schema notes, metric definitions, redacted samples, or a quick walkthrough are usually enough to decide whether there is a fit.

How is data access handled if we work together?

If the next step is a diagnostic or implementation engagement, access stays client-controlled and is scoped later through least-privilege roles, NDA, procurement, and security review. Our terms and privacy policy are legal context, not a substitute for the access-scoping conversation that follows.

What happens after the call?

If there is a fit, the next step is usually a light entry diagnostic, the AI-Ready Data Diagnostic, a scoped services engagement, or a short implementation recommendation tied to the decision you need to make. If the issue is smaller than that, I will tell you that too.